Spatial Patterns Of Precipitation Research Articles

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

AbstractSub‐seasonal to seasonal (S2S) prediction provides an extended range of lead time for decision‐makers across multiple sectors. S2S forecast is crucial for a country that are susceptible to hydro‐meteorological disasters like Nepal. However, S2S forecast requires assessment with reliable datasets before its application. Since, Nepal lacks a dense station network, multi‐source precipitation estimates (MPEs) are the obvious alternatives. Therefore, using classical evaluation metrics and extreme precipitation indices, this study assessed eleven high‐resolution datasets against 159‐gauge stations over Nepal for the 2001–2020 period. These datasets are classified as gauge‐interpolated (APHRODITE), merged (TPMFD, MSWEP, CLDAS, and CHIRPS), satellite‐based (IMERGV7, IMERGV6, CMORPH, and PERSIANN), and reanalysis (ERA5‐L and HAR). Satellite datasets (except IMERGV7) failed to capture the spatial pattern of mean annual precipitation, while others broadly captured it. Most MPEs struggled to accurately estimate wet season precipitation compared to dry season. Furthermore, increasing estimation error from light to extreme precipitation and decreasing skill metrics from flat to complex terrain, demonstrate the intensity and terrain‐specific limitations of MPEs. In terms of precipitation extremes, APHRODITE exhibits the highest skill, followed by MSWEP, TPMFD, HAR, ERA5‐L, IMERGV7, CLDAS, IMERGV6, CHIRPS, CMORPH, and PERSIANN. IMERGV7 exhibits increased skill in detecting extreme precipitation and precipitation over orography against IMERGV6. APHRODITE and TPMFD datasets performed consistently well in all scales, including weekly anomaly, with an average anomaly correlation of 0.84 and 0.66 respectively. However, since APHRODITE is not widely available, TPMFD can serve as a benchmark dataset for evaluating high‐resolution S2S forecasts within the study region.

Optimal reliability ensemble averaging approach for robust climate projections over China

AbstractAccurate simulation and reliable projection of temperature and precipitation over China under climate change is important for proposing adaptation measures for future natural ecosystems. This study proposes a novel method to construct an optimal reliability ensemble averaging (REA) subset from the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on their historical performance in simulating temperature and precipitation across different subregions. The optimal REA ensemble outperforms the multi‐model ensemble mean (MMEM) and single optimal model in reproducing the spatial patterns of historical annual mean temperature and precipitation over China from 1985 to 2014. Under the examined Shared Socioeconomic Pathway scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), the REA projects persistent warming and increased precipitation towards the end of the 21st century, intensifying under higher emissions. Nationwide mean temperature rises of 1.39, 2.69 and 5.05°C, and precipitation increases of 9%, 10% and 20% are projected in the long‐term (2081–2100) relative to 1995–2014 under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. Northwestern China and the Tibetan Plateau are expected to experience amplified warming and precipitation increases, respectively. Compared to the MMEM, the REA generally indicates reduced warming but larger precipitation increases, especially over the Tibetan Plateau under higher‐emissions scenarios. The REA exhibits lower projection uncertainty than the MMEM for both temperature and precipitation, primarily attributed to reduced internal variability. The novel optimal framework for REA shows the potential for extracting robust regional climate information applicable to different subregions of China. This study may contribute to new comprehension of future climate change over China.

Meteorological disaster disturbances on the main crops in the north‒south transitional zone of China

Global climate change, with warming as its main feature, has altered the spatial-temporal evolution of factors such as precipitation and temperature that can cause meteorological disasters. The complex and changeable climate has led to frequent natural disasters, while the frequency and intensity of extreme climate events have also significantly increased, posing an enormous threat to societal production and human life. As the most important geoecological transitional zone of mainland China, the stability of agricultural production in China’s north–south transitional zone is crucial for ensuring food security under climate change. With the use of daily precipitation and potential evapotranspiration data from 1961 to 2018, this study focused on analysing disturbances such as extreme precipitation and drought disasters at different time scales during the winter wheat and summer maize growing seasons in the north–south transitional zone of China from an agricultural production perspective and attempted to answer the following questions: first, from an agricultural production perspective, what are the temporal and spatial distribution patterns of extreme precipitation and arid climate events in the north–south transitional zone? Second, which areas are at high risk of being disturbed by different types of meteorological disasters and require increased attention? The results indicated that (1) in terms of the overall temporal variation, the degree of extreme precipitation and drought stress faced by agricultural production in the region is decreasing. However, the temporal variation at each station in the north–south transitional zone was not completely consistent with the overall trend, and both increasing and decreasing trends were observed. The sites exhibiting an increase overlapped with typical regions of the north–south transitional zone to varying degrees, indicating that the typical regions represented not only theoretical potential risk areas under climate change but also suffered from meteorological disaster disturbances. (2) The precipitation distribution during the winter wheat growth period in the south–north transitional zone was uneven and varied significantly. High values of extreme precipitation indices during the winter wheat growth period were mainly concentrated in the southern part of the eastern section of the north‒south transitional zone. The precipitation distribution during the summer maize growth period significantly differed, with the highest amount of heavy rain and largest number of rainstorm days concentrated in the southeastern part of the north‒south transitional zone. The spatial distribution of the drought frequency in the north–south transitional zone, as indicated by the monthly standardized precipitation evapotranspiration index (SPEI1), showed that the areas with high total drought frequencies were mainly concentrated in northeast Jiangsu, southeast Henan, and north Anhui, which primarily experienced light drought. The central part of Jiangsu Province exhibited a high frequency of moderate drought, while southern Jiangsu Province and southwestern Shaanxi Province were prone to severe drought. Additionally, southeastern Hebei and eastern Henan were identified as areas with a high frequency of extreme drought. Finally, the central region of Sichuan Province was characterized by both severe and extreme drought conditions. Based on the SPEI12-derived spatial distribution of the drought frequency in the north–south transitional zone, the areas with a high total drought frequency were mainly concentrated in central and eastern Henan, southeast Shaanxi, southeast Shandong, and central Sichuan, which primarily experienced light to moderate drought. The northwestern part of Jiangsu, the southern part of Hebei, and the western part of Shandong are regions with a high frequency of severe drought, while the eastern part of Henan is an area with high frequencies of both severe and extreme drought. (3) High-value areas of extreme precipitation and drought disturbance in the north–south transitional zone overlapped with the edge of the transitional zone to varying degrees. Approximately 63.58% of the north‒south transitional zone of China was characterized by moderate or high stress levels, primarily concentrated along the southern boundary and central core area, and nearly 39.5% of all counties experienced two or more types of disaster stresses.

Spatial downscaling analysis of GPM IMERG precipitation dataset based on multiscale geographically weighted regression model: a case study of the Inner Mongolia Reach of the Yellow River basin

The Inner Mongolia Reach of the Yellow River Basin is characterized by a relative scarcity of meteorological stations. While satellite precipitation products can complement observations from meteorological stations, their limited spatial resolution restricts their efficacy in regional studies. This study utilizes the GPM IMERG precipitation dataset, considering various factors that influence the spatial distribution of precipitation, such as the Normalized Difference Vegetation Index (NDVI), elevation, slope, aspect, and topographical relief, to construct a multiscale geographically weighted regression (MGWR) model. A spatial downscaling method for the GPM IMERG precipitation dataset is proposed, and its reliability is validated through an accuracy assessment. Moreover, the scale differences in the impact of different factors on the spatial pattern of precipitation in the Inner Mongolia Reach of the Yellow River Basin are scrutinized. The results indicate that: 1) The downscaled GPM IMERG precipitation data (1 km × 1 km) exhibit enhanced accuracy compared to the pre-downscaled data (approximately 11 km × 11 km). The correlation coefficient, Bias, and RMSE of the annual precipitation data after downscaling of GPM IMERG are 0.865, 6.05%, and 68.50 mm/year, respectively. For the monthly downscaled precipitation data, the correlation coefficient, Bias, and RMSE are 0.895, 6.09%, and 16.25 mm/month, respectively. The downscaled GPM IMERG precipitation dataset exhibit high accuracy on both annual and monthly temporal scales. 2) Different factors demonstrate localized effects on precipitation in both dry and wet years. Elevation is the dominant factor influencing the spatial heterogeneity of annual precipitation. The findings from this study can provide technical support for hydrological modeling, drought monitoring, and water resource management in data-scarce areas of the Inner Mongolia Reach of the Yellow River Basin.

Inter‐Comparison of Precipitation Simulation and Future Projections Over China From an Ensemble of Multi‐GCM Driven RCM Simulations

AbstractAn analysis is presented of the precipitation bias and change signal in an ensemble of regional climate model (RCM) (RegCM4) projections driven by multiple general circulation models (GCMs) over China. RegCM4 is driven by five different GCMs for the 120‐year period 1979–2099 at 25 km grid spacing, under the representative concentration pathway RCP8.5. We find that the GCMs and RegCM4 reproduce the general spatial pattern of precipitation over China in all four seasons, with RegCM4 providing greater spatial detail, especially over areas with complex terrain. The spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by an underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in northern China, most pronounced in the Northwest basins, by both the GCMs and RegCM4 in all seasons except summer, when more mixed results are found. In addition, weak correlations of the projected change patterns are found in summer between the GCMs and nested RegCM4, indicating the greater role played by the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing.

Effects of land use and land cover change under shared socioeconomic pathways on future climate in the Yellow River basin, China

The Yellow River basin of China has experienced significant land use and land cover change (LUCC) due to excessive exploitation of nature resources, ecological degradation, and rapid urbanization, which plays an important role in the regional climate. It is crucial to explore the climate patterns of the basin under different future development scenarios to mitigate climate issues and achieve “carbon peaking and carbon neutrality goals”. Here we utilized land use and land cover (LULC) data and projections of future climate under two shared socioeconomic path – representative concentration path (SSP245 and SSP585) scenarios. Using the Weather Research and Forecasting (WRF) model, we simulated four future spatial variation patterns of temperature and precipitation in the basin. Results indicated that under the SSP245 and SSP585 scenarios, the temperature is projected to increase by 0.18 °C and 0.46 °C, respectively, while precipitation is expected to rise by 32.21 mm and 134.24 mm, respectively. The impact of LUCC was found to be relatively minor and mainly concentrated in the middle reaches of the basin. It resulted in a slight increase in temperature in both scenarios and an increase in precipitation in SSP245, but a decrease in precipitation in SSP585. Changes in farmland and urban area exhibited a certain warming effect in both scenarios, with urban areas having a greater influence, leading to a temperature increase by 0.25 °C and 0.28 °C, respectively. Forest, grassland, and bare areas had a lesser impact on temperature and showed different trends under two scenarios. Regarding precipitation, forests and urban areas had a greater influence in both SSP245 and SSP585 scenarios. This study identified the significant role of LUCC under different development scenarios in shaping future temperature and precipitation changes, providing valuable insights for effectively addressing climate issues in the Yellow River basin. It also highlights the need for clear policy recommendations and identifies institutions or agencies responsible for implementing such recommendations.

Contrasted changes of sea-surface salinity in the southern and northern Okinawa Trough since the mid-Holocene

A reconstruction of sea-surface salinity (SSS) in the southern Okinawa Trough (OT) since 6800 cal. yr BP has been presented in this study. We found that the SSS record increased gradually from the middle to late Holocene, indicating reduced summer precipitation in Taiwan. Our results also suggest regional patterns of SSS with SSS increasing in the southern OT and decreasing in the northern OT since the mid-Holocene. Observational data indicate that the SSS in the southern and northern OT are strongly affected by fluvial discharge from Taiwan and the Changjiang River, linked to summer precipitation in South and Central Eastern China, respectively. The contrasting changes in SSS from the OT since the mid-Holocene suggest different spatial patterns of precipitation, with decreasing summer precipitation in South China and increasing summer precipitation in central-eastern China due to the weakened East Asian Summer Monsoon (EASM) and solar insolation. The delayed seasonal transitions of the EASM, with a longer Meiyu stage and a shorter midsummer stage from the mid-Holocene to late Holocene, are consistent with the delayed northward migration of the westerlies in the late Holocene.

Understanding the Spatiotemporal Variability of Tropical Orographic Rainfall Using Convective Plume Buoyancy

Abstract Mechanical forcing by orography affects precipitating convection across many tropical regions, but controls on the intensity and horizontal extent of the orographic precipitation peak and rain shadow remain poorly understood. A recent theory explains this control of precipitation as arising from modulation of lower-tropospheric temperature and moisture by orographic mechanical forcing, setting the distribution of convective rainfall by controlling parcel buoyancy. Using satellite and reanalysis data, we evaluate this theory by investigating spatiotemporal precipitation variations in six mountainous tropical regions spanning South and Southeast Asia, and the Maritime Continent. We show that a strong relationship holds in these regions between daily precipitation and a measure of convective plume buoyancy. This measure depends on boundary layer thermodynamic properties and lower-free-tropospheric moisture and temperature. Consistent with the theory, temporal variations in lower-free-tropospheric temperature are primarily modulated by orographic mechanical lifting through changes in cross-slope wind speed. However, winds directed along background horizontal moisture gradients also influence lower-tropospheric moisture variations in some regions. The buoyancy measure is also shown to explain many aspects of the spatial patterns of precipitation. Finally, we present a linear model with two horizontal dimensions that combines mountain wave dynamics with a linearized closure exploiting the relationship between precipitation and plume buoyancy. In some regions, this model skillfully captures the spatial structure and intensity of rainfall; it underestimates rainfall in regions where time-mean ascent in large-scale convergence zones shapes lower-tropospheric humidity. Overall, these results provide new understanding of fundamental processes controlling subseasonal and spatial variations in tropical orographic precipitation.

Hydroclimatic changes in eastern China during the Holocene based on pollen data and climate modeling

The Asian monsoon system represents one of the world's most dynamic interactions of the cryosphere-continent-ocean-atmosphere system. Understanding and responding to changes in monsoonal precipitation is a major component of environmental management in this region, given its profound influence on socioeconomic activity in monsoonal Asia. In particular, characterizing the spatiotemporal variability of the East Asian summer monsoon (EASM) is critical for comprehensively understanding its dynamics and future impacts. Although substantial progress has been made in reconstructing past changes in the EASM, the spatial pattern of EASM intensity in China on the orbital-scale remains controversial. This is mainly because of uncertainties in the climatic interpretation of EASM-related proxies. Here, we present a quantitative seasonal precipitation reconstruction for monsoonal China during the Holocene, using the Modern Analog Technique (MAT) based on pollen data. The results show that during the Holocene both the annual and summer precipitation in monsoonal China increased gradually and reached a peak during the early mid-Holocene, with respective values that were ∼ 170 mm and 60 mm higher than today, followed by a decrease in the late Holocene. On the regional scale, the temporal pattern of monsoon rainfall was discrepant: a decrease from the early through late Holocene in northern China, and an increase from the early through late Holocene in southern China.To investigate the possible forcing mechanisms of these phenomena, we used the full TraCE-21 k simulation and single-forcing transient simulations to analyze spatial and temporal rainfall patterns. The results suggest that the north-south pattern is the product of orbital forcing. Intensified summer insolation during the early Holocene enhanced the land-sea thermal contrast, which strengthened the EASM along with the northward extension of the west Pacific subtropical high. Both factors caused the increased withdrawal of oceanic water vapor and the northward advance of the EASM rain belt, delivering more rainfall to northern China and less rainfall to southern China, and vice versa. Overall, our results indicate that insolation variations were the dominant factor determining the spatial pattern of EASM precipitation during the Holocene. Our findings are relevant to the calibration of palaeoclimate models and they contribute to an improved understanding of the Asian monsoon sub-systems.

Characterizing variability of spatial patterns of annual and seasonal precipitation of Turkey and identifying the probable driving factors including teleconnection patterns

Abstract This study aimed to identify primary modes of annual and seasonal precipitation in Turkey using a rotated Empirical Orthogonal Function (EOF) method, and the resulting patterns were described concerning the prevalent atmospheric circulations, orography, and continentality. The varimax rotation of the EOF determines modes that are more localized in space than the conventional EOF modes The first three EOFs accounted for approximately 67% and 62% of the total variance in the annual and wet season precipitation series, respectively, whereas only 50% of the variance was captured by the first three EOFs for dry season precipitation. Spatially different atmospheric circulation mechanisms are major drivers of variability in Turkish precipitation on annual and seasonal timescales. The spatial coherence of the highest negative and positive EOF1 loadings of the annual data was observed in the western and southern regions where westerly and northwesterly circulations prevailed during the wet season. A lower spatial coherency was observed with the dry season precipitation. The contribution of atmospheric moisture advection to precipitation variability diminishes in summer, whereas that of local land surface processes increases. Some regional teleconnection patterns, such as the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO), also contributed to the annual variability in precipitation.

Downscaling and merging multiple satellite precipitation products and gauge observations using random forest with the incorporation of spatial autocorrelation

Publicly available satellite precipitation products (SPPs) are essential inputs for hydrological models. However, the existing SPPs suffer from coarse resolutions and large uncertainties, which greatly hinder their widespread applications. Thus, to fully utilize the unique characteristics of each individual SPP, a spatial random forest (SRF)-based downscaling and merging (SRF-DM) method is proposed to fuse multiple SPPs and gauge observations in this paper. The proposed method incorporates spatial autocorrelation information between precipitation observations through a covariate. This covariate is estimated using ordinary kriging interpolation on spatial neighbors that have a high correlation with the target location. The proposed method was used to generate 1-km daily precipitation data using the datasets from five satellite precipitation products (IMERG, GSMaP, CHIRPS, MORPH, and PERSIANN) and gauge observations in the period from January 1, 2015 to December 31, 2019 over Sichuan province, China. The performance of SRF-DM was compared with the original SPPs, XGBoost-based downscaling and merging (XGB-DM), two double-layer machine learning (ML) methods, namely RF-RF and RF-artificial neural network (RF-ANN), and three single ML methods, namely RF, ANN, and RF-based Merging Procedure (RF-MEP). The experimental results demonstrate the following findings: (i) SRF-DM is more accurate than the original SPPs across different temporal scales (i.e., daily, monthly, and seasonal) in terms of statistical and categorical accuracy measures, (ii) SRF-DM outperforms the six ML-based methods in estimating high precipitation and capturing spatial precipitation patterns, (iii) the spatial autocorrelation between neighboring precipitation data is the most significant factor in both downscaling and merging, and (iv) the performance of SRF-DM is influenced by gauge density, but it still yields good results even with a limited number of gauges. On the whole, SRF-DM is considered an effective tool for generating daily precipitation products with high accuracy and resolution, which is primarily attributed to its satisfactory performance in regions with diverse precipitation patterns and topography.

Water vapour exchange between the atmospheric boundary layer and free troposphere over eastern China: seasonal characteristics and the El Niño–Southern Oscillation anomaly

Abstract. This study develops a quantitative climatology of water vapour exchange between the atmospheric boundary layer (ABL) and free troposphere (FT) over eastern China. The exchange flux is estimated for January, April, July and October over 7 years based on a water vapour budget equation using simulated meteorological data. The spatiotemporal characteristics and occurrence mechanism of ABL–FT water vapour exchange and its relationship with the El Niño–Southern Oscillation (ENSO) are revealed: (1) the vertical exchange flux varies regionally and seasonally, with downward transport to maintain ABL moisture during winter and autumn in the northern region and persistent output to humidify the FT in the southern region, particularly in summer. Additionally, the vertical exchange flux is also topographic dependent. (2) The vertical motion at the ABL top, which is produced by the dynamic forcing of the terrain on synoptic winds, is the dominant mechanism for the water vapour vertical exchange over the long-term average. The evolution of the vertical exchange flux within 1 d scale is driven by the ABL diurnal cycle. (3) The interannual variation of water vapour vertical exchange is correlated with ENSO. A triple antiphase distribution with negative–positive–negative anomalies from north to south exists in La Niña years (and vice versa in El Niño years), which corresponds to the spatial pattern of anomalous precipitation. This phenomenon is mainly due to the alteration of vertical velocity and water vapour content at the ABL top varying with ENSO phases. These results provide new insight into understanding the atmospheric water cycle.

Study on spatiotemporal dynamic characteristics of precipitation and causes of waterlogging based on a data-driven framework

The discernible alterations in regional precipitation patterns, influenced by the intersecting factors of urbanization and climate change, exert a substantial impact on urban flood disasters. Based on multi-source precipitation data, a data-driven model fusion framework was constructed to analyze the spatial and temporal dynamic distribution characteristics of precipitation in Beijing. Wavelet analysis method was used to reveal the periodic variation characteristics and multi-scale effects of precipitation, and the machine learning method was used to characterize the spatiotemporal dynamic change pattern of precipitation. Finally, geographical detector was used to explore the causes of waterlogging in Beijing. The research outcomes reveal a disparate distribution of precipitation across the year, with 78 % of the total precipitation occurring during the flood season. The principal periodic cycles observed in annual cumulative precipitation (ACP) were identified at 21, 13, and 9-year intervals. Spatially, while a decreasing trend in precipitation was observed in most areas of Beijing, 63.4 % of the region exhibited an escalating concentration trend, thereby heightening the risk of urban waterlogging. Machine learning model clustering elucidated three predominant spatial dynamic distribution patterns of precipitation in Beijing. The utilization of web crawler technology to acquire water accumulation data addressed challenges in obtaining urban waterlogging data, and validation through Landsat8 images enhanced data reliability and authenticity. Factor detection shows that road network density, topography, and precipitation were the main factors affecting urban waterlogging. These findings hold significant implications for informing flood control strategies and emergency management protocols in urban areas across China.

Understanding the Influence of Urban Form on the Spatial Pattern of Precipitation

AbstractUrban areas are known to modify the spatial pattern of precipitation climatology. Existing observational evidence suggests that precipitation can be enhanced downwind of a city. Among the proposed mechanisms, the thermodynamic and aerodynamic processes in the urban lower atmosphere interact with the meteorological conditions and can play a key role in determining the resulting precipitation patterns. In addition, these processes are influenced by urban form, such as the impervious surface extent. This study aims to unravel how different urban forms impact the spatial patterns of precipitation climatology under different meteorological conditions. We use the Multi‐Radar Multi‐Sensor quantitative precipitation estimation data products and analyze the hourly precipitation maps for 27 selected cities across the continental United States from the years 2015–2021 summer months. Results show that about 80% of the studied cities exhibit a statistically significant downwind enhancement of precipitation. Additionally, we find that the precipitation pattern tends to be more spatially clustered in intensity under higher wind speed; the location of radial precipitation maxima is located closer to the city center under low background winds but shifts downwind under high wind conditions. The magnitude of downwind precipitation enhancement is highly dependent on wind directions and is positively correlated with the city size for the south, southwest, and west directions. This study presents observational evidence through a cross‐city analysis that the urban precipitation pattern can be influenced by the urban modification of atmospheric processes, providing insight into the mechanistic link between future urban land‐use change and hydroclimates.

Analysis of the Impact of Drought Characteristics Change on Natural Ecological Environment in the West of Hexi Corridor

The plant communities and wild animals in the Gobi desert outside the oases in the west of Hexi Corridor are an indispensable and important part of the human living environment. Affected by global warming, the spatial and temporal distribution patterns of precipitation have changed significantly. This article uses daily precipitation data from 6 hydro-logical stations in the middle and lower reaches of the Shule River Basin from 1957 to 2018, and daily precipitation data from 3 meteorological stations from 1980 to 2018. Through research on annual scale precipitation anomaly percentage, effective precipitation days and annual distribution, and drought station frequency ratio and other parameters, it is shown that there is a significant change in precipitation distribution within the year. The distribution of effective precipitation is not conducive to the survival of ecosystems, and seasonal drought is on the rise. The trend of continuous drought in two seasons and three seasons is on the rise, and the evolution of drought has a significant impact on the natural ecological environment. Effective rainfall is a key element in the rise and fall, distribution, and change of natural ecosystems. Studying the impact of changes in drought characteristics on natural ecology is of great significance for actively responding to drought disasters and maintaining basic ecology.

Understanding precipitation changes through unsupervised machine learning

Abstract Despite the importance of quantifying how the spatial patterns of heavy precipitation will change with warming, we lack tools to objectively analyze the storm-scale outputs of modern climate models. To address this gap, we develop an unsupervised, spatial machine-learning framework to quantify how storm dynamics affect changes in heavy precipitation. We find that changes in heavy precipitation (above the 80th percentile) are predominantly explained by changes in the frequency of these events, rather than by changes in how these storm regimes produce precipitation. Our study shows how unsupervised machine learning, paired with domain knowledge, may allow us to better understand the physics of the atmosphere and anticipate the changes associated with a warming world.

An increase in widespread extreme precipitation events during the northeast monsoon season over south peninsular India

While the spatio-temporal characteristics of Indian summer monsoon precipitation and its extreme spells have been extensively studied, the northeast monsoon, which occurs from October to December (i.e., post-monsoon season) and affects the southern peninsula of India, has not received as much attention. In light of this, the present study explores the spatio-temporal characteristics of precipitation during the northeast monsoon, with a particular emphasis on widespread extreme precipitation events and their associated large-scale synoptic systems, using recent ensemble of high-resolution regional climate models (RCMs) simulations and the Indian monsoon data assimilation and analysis (IMDAA) reanalysis. The study reveals that both models tend to underestimate the intensity and frequency of observed precipitation events, although their skills in reproducing the observed spatial patterns of both mean and extreme precipitation are quite high (r > 0.75). A substantial increase in widespread extreme precipitation events (nearly twofold), along with a 30% rise in precipitation intensity, has been observed in the recent decade compared to the 1980s, and models demonstrate a similar directional change but tend to underestimate the magnitude of observed precipitation. This increase appears to be linked to the rapid warming of the Indian Ocean, which, in turn, increases the water vapor in the atmosphere, ultimately supplying more moisture to the southeastern peninsular India. On the other hand, observed discrepancies in replicating some of the reported widespread impactful extreme precipitation events in the years 2007 and 2015 over the southern India region underscore the need for caution when interpreting model simulations. Low-pressure systems, such as troughs, associated with cyclonic circulations originating from the Bay of Bengal, have been identified as the primary sources of moisture fueling heavy precipitation during these events. Cluster analysis highlights varying synoptic patterns within the general framework, emphasizing the need for a more nuanced approach in simulating and forecasting extreme precipitation events. Overall, this study underscores the importance of enhancing modeling capabilities to better understand and prepare for the growing challenges posed by extreme precipitation events.

Observed changes in hydroclimate attributed to human forcing

Observational and modeling studies indicate significant changes in the global hydroclimate in the twentieth and early twenty-first centuries due to anthropogenic climate change. In this review, we analyze the recent literature on the observed changes in hydroclimate attributable to anthropogenic forcing, the physical and biological mechanisms underlying those changes, and the advantages and limitations of current detection and attribution methods. Changes in the magnitude and spatial patterns of precipitation minus evaporation (P–E) are consistent with increased water vapor content driven by higher temperatures. While thermodynamics explains most of the observed changes, the contribution of dynamics is not yet well constrained, especially at regional and local scales, due to limitations in observations and climate models. Anthropogenic climate change has also increased the severity and likelihood of contemporaneous droughts in southwestern North America, southwestern South America, the Mediterranean, and the Caribbean. An increased frequency of extreme precipitation events and shifts in phenology has also been attributed to anthropogenic climate change. While considerable uncertainties persist on the role of plant physiology in modulating hydroclimate and vice versa, emerging evidence indicates that increased canopy water demand and longer growing seasons negate the water-saving effects from increased water-use efficiency.

Improving daily precipitation estimations in a high mountainous watershed by developing a new downscaling method with spatially varying coefficients

Accurate estimates of spatial patterns of daily precipitation are critically important for numerous hydrologic applications. However, the estimation of daily precipitation is still challenging, especially in regions with sparse observation networks and complex terrain. Although satellite precipitation products can offer continuous precipitation fields on a daily scale, the data contain large bias and coarse spatial resolution, which limit the application of these products in local studies. Here, we developed a new spatially varying coefficient downscaling approach to yield a daily precipitation field with high-resolution and high-accuracy in a high mountainous watershed. First, the random effect eigenvector spatial filtering (RESF) model was introduced and improved by considering the nonlinear term; second, the residuals were corrected by applying the simple inverse distance weighted (IDW) method. Using the proposed method, named RESF-IDW, the Global Satellite Mapping of Precipitation (GSMaP) was downscaled from 0.1° to 0.01° by integrating reanalyzed atmospheric data and environmental variables as auxiliary information. The performance of RESF-IDW was evaluated and compared with the geographically weighted regression (GWR) method and the original GSMaP precipitation products at 20 national and automatic stations by using leave-one-out cross validation. The results show that compared to the original GSMaP products, RESF-IDW, RESF, GWR-IDW, and GWR exhibited improvements in their mean values of correlation coefficient (CC) by 24.5%, 17.9%, 12.0%, and 3.2%, respectively. Additionally, their mean values of mean absolute error (MAE) improved by 31.2%, 14.9%, 18.7%, and 2.4%, respectively, while their mean values of root mean square error (RMSE) improved by 24.1%, 18.5%, 9.6%, and 3.8%, respectively. The RESF-IDW model can be used to correctly improve the original GSMaP product under different precipitation intensities, with critical success index (CSI) improvements of 38.81%, 43.14% and 44.58% for low, medium and high precipitation intensities, respectively. The use of residual correction enhances the spatial accuracy (MAE decreases by 19.43% on average) and the precipitation detection capability (CSI increases by 12.09% on average) of the RESF estimations. Based on the error metrics, the RESF-IDW model can be used to generate improved precipitation estimates on a daily scale and has a good capability for precipitation detection over different intensities. The proposed method in this study provides a promising way to generate accurate daily precipitation fields with high spatial resolution over data sparse and high mountain regions.

Performance Evaluation of GPM IMERG Precipitation Products over the Tropical Oceans Using Buoys

Abstract The majority of global precipitation falls in tropical oceans. Nonetheless, due to the lack of in situ precipitation measurements, the number of studies over the tropical oceans remains limited. Similarly, the performance of IMERG products over the tropical oceans is unknown. In this context, this study quantitatively evaluates the 20 years (2001–20) of IMERG V06 Early, Late, and Final products against the in situ buoys’ estimates using the pixel–point approach at a daily scale across the tropical oceans. Results show that IMERG represents well the mean spatial pattern and spatial variation of precipitation, though significant differences exist in the magnitude of precipitation amount. Overall, IMERG notably overestimates precipitation across the tropical ocean, with maxima over the western Pacific and Indian Oceans, while it performs better over the eastern Pacific and Atlantic Oceans. Moreover, irrespective of the region, IMERG sufficiently detects precipitation events (i.e., >0.1 mm day−1) for high-precipitation regions, though it significantly overestimates the magnitude. Despite IMERG’s detection issues of precipitation events over the regions with lower precipitation, it is in good agreement with the buoys in total precipitation estimation. The positive hit bias and false alarm bias are the major contributions to the overall total positive bias. Furthermore, the detection capability of IMERG tends to decline with increasing precipitation rates. In terms of IMERG runs, the IMERG Final product performs slightly better than the Early and Late runs. More detailed studies over the tropical oceans are required to better characterize the biases and their sources.